1. Field of the Invention
The present invention is directed to a method, apparatus and computer program product for adaptive filtering of projection data acquired by means of a medical diagnosis apparatus.
2. Description of the Prior Art
Projection data of an examined measurement subject can be acquired with modern medical diagnosis methods such as, for example, computed tomography. Generally, the examined measurement subject is a patient.
The acquired projection data usually are in digital form and are thus accessible to digital data processing. A digitization is initially needed, given analog projection data.
Possible operations of the digital data processing are, for example, an amplification, overlaying or filtering.
Since a number of manipulated variables can enter into the acquired projection data dependent on the measurement method and the diagnostic apparatus employed, the acquired projection data can comprise a plurality of dimensions.
In modern computed tomography (CT), the parameter 1 in the measured data S (l, k, z) indicates the projection angle (the angular position of the tube or, angular position of tube and detector system), k indicates the channel index (corresponding to the angle in the fan, given fan geometry and corresponding to the distance of the beam from the rotational center in parallel geometry), and z indicates the slice.
These acquired projection data (slice dataset) are then reconstructed with a reconstruction method for planar data (usually filtered back-projection or Fourier reconstruction) in order to obtain the desired CT image.
In practically all medical diagnostic devices the image quality with reference to noise and low-contrast perceptibility increases monotonously with the patient dose. For improving the image quality, thus, an increase in the dose stress on the patient is generally required. Such an increase in, the patient dose is possible only to a limited extent in order to avoid secondary harm.
Digital data processing offers an alternative possibility for reducing the pixel noise. For example, smoothing reconstruction filters can be freely selected within certain limits without significant technical outlay in any commercially available CT apparatus, so that the noise level in the image can be lowered by means of smoothing filtering without increasing the patient dose.
A disadvantage of smoothing reconstruction filters, however, is that the entire dataset is smoothed with this method. This necessarily leads to a degradation of the spatial resolution.
Approaches for adaptive filtering of the measured data are found in the literature for solving this problem, i.e. the dataset is not globally smoothed, but only locally smoothed (Jiang, “Adaptive trimmed mean for computer tomography image reconstruction, Proc. of SPIE, 2299, pp. 316-324, 1994; Jiang, “Adaptive filtering approach to the streaking artifact reduction due to x-ray photon starvation”, Radiology 205 (P), p. 391, 1997; Berkman Sahiner and Andrew E. Yagle, “Reconstruction from projections under timefrequency constraints”, IEEE Transactions on Medical Imaging, 14(2), pp. 193-204, 1995).
Usually, the acquired projection data of detector elements neighboring in the k-direction are employed for the adaptive filtering. The filtering thus occurs exclusively in the l-direction.
German PS 198 53 143 also discloses a computed tomography apparatus wherein the noise level of the interpolated projections does not exceed a specific threshold by means of 3D adaptive filtering both in the channel-direction (ξ-coordinate), in projection direction (v-coordinate) as well as in the table feed direction (z-coordinate) according to the equationρAF(ξ,v,z)=∫dξ′dv′dz′∫Δξ(ξ−ξ′)∫Δv(v−v′)∫Δz(z−z′)ρx(ξ′,v′,z′). ρx(ξ,v,z)denotes the projection data in parallel or fan geometry available before the implementation of the adaptive filtering, PAF (ξ,v,z) denotes the projection data in parallel or fan geometry available after the implementation of the adaptive filtering, and Δξ, Δv, Δz denote the filter widths in the three coordinate directions.
These filter widths are a function of the projection value ρx(ξ, v, z) (adaptive filtering) to be currently filtered: Δξ=Δξ(ρx(ξ, v, z)), Δv=Δv(ρx(ξ, v, z)) and Δz=Δz(ρx(ξ,v, z)). fΔξ(·), fΔv(·) and fΔz(·) reference the filter function (axially symmetrical with values ≧0 and total area 1) for the smoothing in the respective coordinates. The filter widths Δξ, Δv, and Δz respectively represent the half intensity widths or some other characteristic width criterion of the filter functions. When one or more of the widths is/are zero, then the filter function is reduced to a Dirac delta function and no filtering occurs in the corresponding coordinates.
German PS 198 53 143 thus discloses a method for filtering multi-dimensional planar projection data (attenuation values) of a CT scan wherein the adaptation of the filter to the projection data under consideration ensues by variation of the width of the filter kernel (filter width) in the individual dimensions.
A disadvantage of this known method is that the implementation of the method requires considerable computing and time expenditure due to the adaptively fluctuating filter width in the individual dimensions.